There exist a multitude of applications, both Internet-based applications and smartphone/tablet-based applications, that make recommendations for users based on analyzing a user's history. A user's history can include or reflect, for example, choices the user previously made based on the user's preferences. Although a user's preferences can be constant as a whole over a long period of time, the generality of the preferences allow for a user's current specific preference to be less defined. For example, user's preferences with respect to food are fairly constant. A user may prefer, for example, Italian food and Chinese food. A history of the user's food preferences captures this general information and may allow applications to provide a generalized recommendation for what a user may want now or in the future. However, a user's current preferences can be more granular than what can be captured by recommendation systems that rely on analyzing a user's history to make a current recommendation at a specific moment in time when the user may be craving something in particular. Thus, the granularity of a user's current, specific preference also allows for specific current interests than cannot accurately be predicted based on a user's history alone. For example, with respect to food, a user can experience cravings—where a user desires a specific type of food among all of the types of food that the user normally enjoys. Therefore, current applications do not help a user determine what the user's current interest is despite the applications' having access to the user's history. Moreover, although the user may know that he or she wants something, the particular object of the user's interest may be unknown even to the user until one of the user's senses is inspired or provoked. Further, thinking by the user of what the users' current interest is, alone, may not help the user in defining his or her current interest.
According to aspects of the present disclosure, a system and computer-implemented method are disclosed that guide a user to his or her current interest based on a sequential presentation of images representative of possible physical objects of interest.